Julio Godoy,
Ioannis Karamouzas,
Stephen J. Guy,
Maria Gini,
In multi-robot systems, efficiently navigating in a
a partially-known environment is an ubiquitous but challenging
task, as each robot must account for the uncertainty introduced,
for example, by other moving robots. This uncertainty makes
pre-computed plans not always applicable, and often hinders
the desired efficient use of the robot’s resources. In this work, we
present a local anytime approach for robot motion planning that
accounts for the uncertainty of the environment by generating
‘snapshots’ of possible future scenarios. Our approach adapts
the Hindsight optimization technique to allow robots to plan
their immediate motion based on long-term efficiency. We
validate our approach by comparing the efficiency on the paths
executed against a state-of-the art navigation technique in a
variety of scenarios, and show that by accounting for the
uncertainty in the environment, agents can improve their time- and
energy-efficient motions.
